Integration of Multiple Data Sets with Clustering Techniques


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Documentation for package ‘IntClust’ version 0.1.0

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IntClust-package Integrated Clustering Methods.
ABC.SingleInMultiple Single-source ABC clustering
ADC Aggregated data clustering
ADEC Aggregated data ensemble clustering
BinFeaturesPlot_MultipleData Visualization of characteristic binary features of multiple data sets
BinFeaturesPlot_SingleData Visualization of characteristic binary features of a single data set
BoxPlotDistance Box plots of one distance matrix categorized against another distance matrix.
CEC Complementary ensemble clustering
CharacteristicFeatures Determining the characteristic features of a cluster
ChooseCluster Interactive plot to determine DE Genes and DE features for a specific cluster
Cluster Single source clustering
ClusterCols Matching clusters with colours
ClusteringAggregation Clustering aggregation
ClusterPlot Colouring clusters in a dendrogram
ColorPalette Create a color palette to be used in the plots
Colors1 Colour examples
ColorsNames Function that annotates colors to their names
CompareInteractive Interactive comparison of clustering results for a specific cluster or method.
ComparePlot Comparison of clustering results over multiple results
CompareSilCluster Compares medoid clustering results based on silhouette widths
CompareSvsM Comparison of clustering results for the single and multiple source clustering.
ConsensusClustering Consensus clustering
ContFeaturesPlot Plot of continuous features
CVAA Cumulative voting-based aggregation algorithm
DetermineWeight_SilClust Determines an optimal weight for weighted clustering by silhouettes widths.
DetermineWeight_SimClust Determines an optimal weight for weighted clustering by similarity weighted clustering.
DiffGenes Differential gene expressions for multiple results
DiffGenesSelection Differential expression for a selection of objects
Distance Distance calculation
distanceheatmaps Determine the distance in a heatmap
EHC Ensemble for hierarchical clustering
EnsembleClustering Ensemble clustering
EvidenceAccumulation Evidence accumulation
f.clustABC.MultiSource f.clustABC.MultiSoucre
f.gsample f.gsample
f.rmv f.rmv
f.t ff
FeatSelection feature selection for a selection of objects
FeaturesOfCluster List all features present in a selected cluster of objects
FindCluster Find a selection of objects in the output of 'ReorderToReference'
FindElement Find an element in a data structure
FindGenes Investigates whether genes are differential expressed in multiple clusters
fingerprintMat Fingerprint data
GeneInfo Information of the genes Gene info in a data frame
geneMat Gene expression data
Geneset.intersect Intersection over resulting gene sets of 'PathwaysIter' function
Geneset.intersectSelection Intersection over resulting gene sets of 'PathwaysIter' function for a selection of objects
GS List of GO Annotations
HBGF Hybrid bipartite graph formulation
HeatmapPlot Comparing two clustering results with a heatmap
HeatmapSelection A function to select a group of objects via the similarity heatmap.
HierarchicalEnsembleClustering Hierarchical ensemble clustering
IntClust Integrated Clustering Methods.
LabelCols Colouring labels
LabelPlot Coloring specific leaves of a dendrogram
LinkBasedClustering Link based clustering
M_ABC Multi-source ABC clustering
Normalization Normalization of features
PathwayAnalysis Pathway Analysis
Pathways Pathway analysis for multiple clustering results
PathwaysIter Iterations of the pathway analysis
PathwaysSelection Pathway analysis for a selection of objects
PlotPathways A GO plot of a pathway analysis output.
PreparePathway Preparing a data set for pathway analysis
ProfilePlot Plotting gene profiles
ReorderToReference Order the outputs of the clustering methods against a reference
SelectnrClusters Determines an optimal number of clusters based on silhouette widths
SharedComps Intersection of clusters across multiple methods
SharedGenesPathsFeat Intersection of genes and pathways over multiple methods
SharedSelection Intersection of genes and pathways over multiple methods for a selection of objects.
SharedSelectionLimma Intersection of genes over multiple methods for a selection of objects.
SharedSelectionMLP Intersection of pathways over multiple methods for a selection of objects.
SimilarityHeatmap A heatmap of similarity values between objects
SimilarityMeasure A measure of similarity for the outputs of the different methods
SNF Similarity network fusion
targetMat Target prediction data
TrackCluster Follow a cluster over multiple methods
WeightedClust Weighted clustering
WonM Weighting on membership